Tips for writing the thesis

And here's another link. This one has some useful tips when you (start) writing your thesis, including typesetting, acronyms, and more. See http://staff.science.uva.nl/~vdham/projects/typesetting-thesis.html.

On the history of evaluation in IR

I'm currently writing my PhD thesis and this paper is one of the gems I found: "On the history of evaluation in IR", S. Robertson, Journal of Information Science 34(4), 2008. You can find it here: http://jis.sagepub.com/cgi/content/long/34/4/439 (there's also a citeseer version for those without access I believe).

I think it's a must-read for any PhD student doing IR since it covers the fundamentals (and history!) of evaluation in IR. It's also written in a light and sometimes even funny style.

Enjoy :)

Don't be a Spock - Adjust your monitor height

Some practical guidance for all you guys working on your PhDs: http://www.xadamdx.com/2010/08/dont-be-spock-adjust-your-monitor.html.

3 shell scripts to improve your writing, or "My Ph.D. advisor rewrote himself in bash."

Fun read!

The hardest part of advising Ph.D. students is teaching them how to write.

Fortunately, I've seen patterns emerge over the past couple years.

So, I've decided to replace myself with a shell script.

See: http://matt.might.net/articles/shell-scripts-for-passive-voice-weasel-words-duplicates/.

Your research career: Academia or Industry?

Dr. Chistoph Bartneck, an assistant professor at TU Eindhoven gave this career talk (~12 min.) at the HRI conference in Osaka recently where he discussed the merits and cons of choosing between academia and industry after grad school. It's a truly 'sobering' talk that's extremely relevant to all of us so I had to pass it on. While many of us are generally aware of these differences, the power of the talk comes from tying it all together neatly.

You can watch the talk here.

Knowing your statistics is a fact of scientific life...

...and it's no exception for us PhD students. Below are three resources that have made statistical concepts and their application(s) truly accessible and intelligible to me (though I admit statistics was never really my strength).

Introduction to Statistics and Multivariate Statistics by David W. Stockburger from Missouri State University and Statnotes: Multivariate Statistics by G. David Garson from North Carolina State University.

Some good advice from the guys at Cornell

There's a long list of links with tons of good advice for PhD students at this site: http://www.cs.cornell.edu/People/czars/mentor/. I loved the one about getting your abstract rejected by Mary-Claire van Leunen and Richard Lipton.

Some light reading

If you get to that stage where you need to start The Writeup (aka your PhD dissertation), you might be interested in some tips. See How to Write a Ph.D. Dissertation.

:-)

You and Your Research by R. Hamming

Richard Hamming, a mathematician known for the Hamming Code, the Hamming distance along with numerical methods gave an interesting, and inspiring speech at Bell Labs 7 July 1986: You and Your Research. For those who don't have time do go through the entire speech, a condensed version can be found in: A Stroke of Genius: Striving for Greatness in All You Do.

It stroke me when I read that researchers do not cope with problems for which they don't have an "obvious" approach:

There are many right problems, but very few people search carefully for them. Rather they simply drift along doing what comes to them, following the easiest path to tomorrow. Great scientists all spend a lot of time and effort in examining the important problems in their field. Many have a list of 10 to 20 problems that might be important if they had a decent attack. As a result, when they notice something new that they had not known but seems to be relevant, then they are prepared to turn to the corresponding problem, work on it, and get there first.

I also found comforting statements from the ``big guys'' that one doesn't have to be a genius for making substantial contributions:

Newton said, ``If others would think as hard as I did, then they would get similar results.''

Edison said that genius was 99% perspiration and 1% inspiration.

and let's conclude with the Google Scholar tagline, from Newton:

"If I had seen farther than others it is because I stood on the shoulders of giants."

Happy reading, and researching!

Wolfram Alpha vs. Google

I just read the wired review of the Wolfram Alpha search engine on http://www.wired.com/epicenter/2009/05/wolframalpha-fails-the-cool-test/. They follow an opinion that I've read quite a lot - that Alpha is not going to be serious competition to Google.

Now that I've tried Alpha - I don't think competing with Google is the point really (independently of how Wolfram is trying to position the system himself, I guess there's some PR involved as well).

I agree that Alpha is not very useful as a general purpose search engine. It does different things, for example specify and visualize relations between things. For example, it allows you to compare things. "Amsterdam vs. Berlin" tells you that the number of people living in the metropolitan areas of both cities is quite similar (3 vs. 4 million), and that the flight time between the two cities is about 40 minutes. As people start trying Alpha more, I suspect that they will find a lot of uses, even ones unintended by the creators at this point.